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Numerous academic institutions have embarked on the pursuit of hybrid learning as an alternative approach, catering to students who opt for replacing a fraction of their conventional in-person meeting schedule with remote teaching. However, these new remote learning patterns have brought forth new stands against students' expectations. The universities have come across immense challenges in devising efficacious strategies that encompass the delivery, effectiveness and acceptability of hybrid courses. Consequently, identifying pivotal determinants related to user acceptance of technology persists as a crucial matter. This study aims to shed light on the adoption of hybrid learning from students' perspectives.

Structural equation modelling (SEM) was employed to scrutinise the proposed research model and hypotheses. A total sample of 444 students responded and partook in the survey. The data were analysed using AMOS software, a powerful tool for statistical analysis in the field of social sciences.

The findings of this study show that perceived service quality positively and significantly impacts the ease of use and usefulness of a hybrid learning system among students. In addition, the results demonstrate that ease of use and usefulness of the system positively and significantly influence students' favourable attitudes toward hybrid learning. Remarkably, the statistical analysis unveils the significant mediating effect of ease of use and usefulness in the relationship between perceived service quality and students' attitudes toward hybrid learning.

The findings suggest that the pervasive dependence on information systems and the quality of service from novel technologies continues to be a vital influence in the learning environment. The study has provided valuable insights into student perseverance learning strategies for higher education institutions.

This study's novelty lies in illuminating the crucial role of ease of use and usefulness as mediators, highlighting their criticality in enhancing students' attitudes towards hybrid learning. Notably, the study underscores that perceived service quality exerts a positive influence on ease of use and usefulness.

This study aims to explore the impact of hybrid learning on the satisfaction, effectiveness and academic performance of faculty and students in private universities in Kuwait. The study specifically addresses the challenges and experiences confronted by students and faculty members in utilising hybrid learning, assesses whether hybrid learning leads to improvements in academic performance and proposes measures for providing quality hybrid learning models in post-coronavirus disease 2019 (COVID-19) situations.

This study employed a quantitative method by collecting data via a questionnaire targeting private university students and faculty members in Kuwait. A total of 311 participants completed the survey.

The results show that students were in favour of the hybrid learning method and viewed this as being efficient when coming to satisfaction, academic performance and overall effectiveness; whilst faculty members have voiced the members' disfavour of the hybrid model of teaching as the members believe that the system is unjust, especially when coming to student's academic performance.

Although research related to hybrid learning in different parts of the world has existed for many years, the research only recently gained prominence in Kuwait due to the COVID-19 pandemic. This paper will likely be of interest to academics, policymakers, the government and universities as the pandemic continues to make hybrid learning more popular and, therefore, would increase awareness with regards to students' and faculty members' opinions about the system and proposed ways to resolve any issues.

The COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.

The importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.

The findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.

While the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.

A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes determined by clinical experts. This training procedure is driven by a k‐fold cross‐validation method. Finally, a Bayesian committee machine is formed by training a Bayesian network to combine the classifications of the k neural networks (NNs).

The authors outcomes show high efficiency rates from a two‐stage cascade approach to tissue identification. Giving a non‐homogeneous distribution of pattern classes, this hybrid approach has shown an additional advantage of increasing the classification efficiency when classifying patterns with relative low frequencies.

The methodology and results presented in this paper could have important implications to the field of clinical pressure ulcer evaluation and diagnosis.

The novelty associated with this work is the use of a hybrid approach consisting of NNs and Bayesian classifiers which are combined to increase the performance of a pattern recognition task applied to the real clinical problem of tissue detection under non‐controlled illumination conditions.

The digital age has provided new possibilities for the connected learning. To better understand these opportunities in the school context, the purpose of this paper is to examine what kinds of learning communities emerge in international learning projects and how tools and technologies support students’ inquiries and peer connections.

The participants in this study were one Finnish 6th-grade class (n=17) and one American 7th–8th-grade class (n=16) who communicated through blogs and Skype. The main sources of deductive content analysis are transcribed Skype meetings, the students’ digital artifacts and a supplementary e-questionnaire.

The results of the study indicated that during the academic learning project, a voluntary, friendship-driven peer community emerged. The interaction in the formal contexts focused on sharing the results of local inquiries through Skype and blogs, whereas the friendship-driven community centered on the creation of social bonds through students’ personal devices and social media applications.

The paper models a hybrid learning system that connected academically oriented and friendship-driven participation.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.

The aim of this study is to explain the educational practices that emerged with the concept of “new normal” in an overview. In line with this goal, the experiences of academics who are experts in their fields have been utilized.

This paper draws on both academic and trade literature. Interviews with representatives from schools providing tourism education in North Cyprus were conducted online and in-person. The qualitative research method in the form of the interview method was employed in the research. The research participants consist of 15 academics working in the field of tourism education. Additionally, concerning the selection of these participants, sector experience and applied lecture status were taken into consideration. As the second dimension of the research, an extensive literature review was conducted and the subject was expanded by explaining it with examples from around the world.

It has been revealed in the research that educational institutions and tourist businesses have to constantly renew themselves technologically. The findings obtained from academics include the innovations they use in the “new normal” tourism education in applications such as Hybrid/BL – Blended Learning, VR – Virtual Reality, Artificial intelligence, LMS & In-class response systems, API-based software systems in the courses they teach. The common opinion of academics who want to provide a better education is that educational institutions should always be infrastructurally prepared for such emergencies.

The global COVID-19 pandemic created economic destruction in many countries and brought life to a halt. In the tourism sector, one of the sectors most affected by the COVID-19 problem, various restrictions were imposed on touristic activities. While this situation caused a decrease in demand in the tourism sector, it paved the way for great technological changes and resulted in the adoption of new educational practices in institutions providing tourism education. This paper traces these new development in the face of the pandemic crisis.

The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.

The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.

Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.

The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.

This research provides evidence toward new recommendation methods directed toward LO repositories.

This study aims to introduce an empirical model which incorporates newsgroups, knowledge forums, knowledge assets and knowledge application processes to share organisational knowledge. Therefore it seeks to illustrate an application for integrating knowledge management (KM) into the business process.

The Taiwanese contingent of an international certification body – also a council member of the International Organisation for Standardisation (ISO) – was selected for a case study. A hybrid technology infrastructure was designed and employed to implement the proposed model. Based on knowledge value added validation, the proposed KM model provides a set of new operating systems for sharing knowledge within an organisation.

Although many theories regarding implementation of KM in organisations have been proposed and studied, an application model for practical integration of various modern principles to share organisational knowledge is strategically important. Therefore a model that integrates principal KM applications into the business process, and the measurement of the resulting benefits, has been developed.

Knowledge is a valuable asset for an individual in today's economy; nevertheless the acquisition of such an asset relies heavily on knowledge sharing within an organisation. The author has proposed an exclusive hybrid platform with an empirical process model to address innovative approaches and practical values of KM within an organisation.